Short CV

Cyrill Stachniss is a full professor at the University of Bonn and heads the lab for Photogrammetry and Robotics. Before working in Bonn, he was a lecturer at the University of Freiburg in Germany, a guest lecturer at the University of Zaragoza in Spain, and a senior researcher at the Swiss Federal Institute of Technology in the group of Roland Siegwart. Cyrill Stachniss finished his habilitation in 2009 and received his PhD thesis entitled “Exploration and Mapping with Mobile Robots” supervised by Wolfram Burgard at the University of Freiburg in 2006. From 2008-2013, he was an associate editor of the IEEE Transactions on Robotics, since 2010 a Microsoft Research Faculty Fellow, and received the IEEE RAS Early Career Award in 2013. Since 2015, he is a senior editor for the IEEE Robotics and Automation Letters. He is the spokesperson of the DFG Cluster of Excellence EXC 2070 ”PhenoRob – Robotics and Phenotyping for Sustainable Crop Production” and of the DFG Research Unit FOR 1505 ”Mapping on Demand”. He was furthermore involved in the coordination of EC-funded FP7 and H2020 projects. In his research, he focuses on probabilistic techniques in the context of mobile robotics, navigation, and perception. Central areas of his research are solutions to the simultaneous localization and mapping problem, visual perception, robot learning, agricultural robotics, computer-controlled cars, and unmanned aerial vehicles. He has coauthored over 170 publications.

{The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607

@Article{sa2018rs,
author = {I. Sa and M. Popovic and R. Khanna and Z. Chen and P. Lottes and F. Liebisch and J. Nieto and C. Stachniss and R. Siegwart},
title = {{WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming}},
journal = rs,
year = 2018,
volume = 10,
issue = 9,
url = {http://www.mdpi.com/2072-4292/10/9/1423/pdf},
doi = {10.3390/rs10091423},
abstract = {The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.},
}

K. H. Huang and C. Stachniss, “Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera Through Relative Orientation Estimation and 1-DoF ICP,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2018. [BibTeX][PDF][Video]

Pose estimation and mapping are key capabilities of most autonomous vehicles and thus a number of localization and SLAM algorithms have been developed in the past. Autonomous robots and cars are typically equipped with multiple sensors. Often, the sensor suite includes a camera and a laser range finder. In this paper, we consider the problem of incremental ego-motion estimation, using both, a monocular camera and a laser range finder jointly. We propose a new algorithm, that exploits the advantages of both sensors—the ability of cameras to determine orientations well and the ability of laser range finders to estimate the scale and to directly obtain 3D point clouds. Our approach estimates the five degree of freedom relative orientation from image pairs through feature point correspondences and formulates the remaining scale estimation as a new variant of the iterative closet point problem with only one degree of freedom. We furthermore exploit the camera information in a new way to constrain the data association between laser point clouds. The experiments presented in this paper suggest that our approach is able to accurately estimate the ego-motion of a vehicle and that we obtain more accurate frame-to-frame alignments than with one sensor modality alone.

@InProceedings{huang2018iros,
author = {K.H. Huang and C. Stachniss},
title = {{Joint Ego-motion Estimation Using a Laser Scanner and a Monocular Camera Through Relative Orientation Estimation and 1-DoF ICP}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = 2018,
videourl = {https://www.youtube.com/watch?v=Glv0UT_KqoM},
abstract = {Pose estimation and mapping are key capabilities of most autonomous vehicles and thus a number of localization and SLAM algorithms have been developed in the past. Autonomous robots and cars are typically equipped with multiple sensors. Often, the sensor suite includes a camera and a laser range finder. In this paper, we consider the problem of incremental ego-motion estimation, using both, a monocular camera and a laser range finder jointly. We propose a new algorithm, that exploits the advantages of both sensors---the ability of cameras to determine orientations well and the ability of laser range finders to estimate the scale and to directly obtain 3D point clouds. Our approach estimates the five degree of freedom relative orientation from image pairs through feature point correspondences and formulates the remaining scale estimation as a new variant of the iterative closet point problem with only one degree of freedom. We furthermore exploit the camera information in a new way to constrain the data association between laser point clouds. The experiments presented in this paper suggest that our approach is able to accurately estimate the ego-motion of a vehicle and that we obtain more accurate frame-to-frame alignments than with one sensor modality alone.}
}

Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.

@InProceedings{lottes2018iros,
author = {P. Lottes and J. Behley and N. Chebrolu and A. Milioto and C. Stachniss},
title = {Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = 2018,
url = {http://www.ipb.uni-bonn.de/pdfs/lottes18iros.pdf},
videourl = {https://www.youtube.com/watch?v=C9mjZxE_Sxg},
abstract = {Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.}
}

Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089m and 0.074m, respectively.

@article{jung2018aei,
title = {Automated 3D volumetric reconstruction of multiple-room building interiors for as-built BIM},
journal = aei,
author = {J. Jung and C. Stachniss and S. Ju and J. Heo},
volume = {38},
pages = {811-825},
year = 2018,
issn = {1474-0346},
doi = {10.1016/j.aei.2018.10.007},
_weburl = {http://www.sciencedirect.com/science/article/pii/S1474034618300600},
abstract = {Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089m and 0.074m, respectively.}
}

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.

@InProceedings{milioto2018icra,
author = {A. Milioto and P. Lottes and C. Stachniss},
title = {Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs},
year = {2018},
booktitle = {Proceedings of the IEEE Int. Conf. on Robotics \& Automation (ICRA)},
abstract = {Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.},
url = {https://arxiv.org/abs/1709.06764},
videourl = {https://youtu.be/DXcTkJmdWFQ},
}

Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.

@Article{palazzolo2018drones,
author = {E. Palazzolo and C. Stachniss},
title = {{Effective Exploration for MAVs Based on the Expected Information Gain}},
journal = {Drones},
volume = {2},
year = {2018},
number = {1},
article-number= {9},
url = {http://www.ipb.uni-bonn.de/pdfs/palazzolo2018drones.pdf},
issn = {2504-446X},
abstract = {Micro aerial vehicles (MAVs) are an excellent platform for autonomous exploration. Most MAVs rely mainly on cameras for buliding a map of the 3D environment. Therefore, vision-based MAVs require an efficient exploration algorithm to select viewpoints that provide informative measurements. In this paper, we propose an exploration approach that selects in real time the next-best-view that maximizes the expected information gain of new measurements. In addition, we take into account the cost of reaching a new viewpoint in terms of distance and predictability of the flight path for a human observer. Finally, our approach selects a path that reduces the risk of crashes when the expected battery life comes to an end, while still maximizing the information gain in the process. We implemented and thoroughly tested our approach and the experiments show that it offers an improved performance compared to other state-of-the-art algorithms in terms of precision of the reconstruction, execution time, and smoothness of the path.},
doi = {10.3390/drones2010009},
}

The Flourish project aims to bridge the gap between current and desired capabilities of agricultural robots by developing an adaptable robotic solution for precision farming. Combining the aerial survey capabilities of a small autonomous multi-copter Unmanned Aerial Vehicle (UAV) with a multi-purpose agricultural Unmanned Ground Vehicle (UGV), the system will be able to survey a field from the air, perform targeted intervention on the ground, and provide detailed information for decision support, all with minimal user intervention. The system can be adapted to a wide range of farm management activities and to different crops by choosing different sensors, status indicators and ground treatment packages. The research project thereby touches a selection of topics addressed by ICPA such as sensor application in managing in-season crop variability, precision nutrient management and crop protection as well as remote sensing applications in precision agriculture and engineering technologies and advances. This contribution will introduce the Flourish consortium and concept using the results of three years of active development, testing, and measuring in field campaigns. Two key parts of the project will be shown in more detail: First, mapping of the field by drones for detection of sugar beet nitrogen status variation and weed pressure in the field and second the perception of the UGV as related to weed classification and subsequent precision weed management. The field mapping by means of an UAV will be shown for crop nitrogen status estimation and weed pressure with examples for subsequent crop management decision support. For nitrogen status, the results indicate that drones are up to the task to deliver crop nitrogen variability maps utilized for variable rate application that are of comparable quality to current on-tractor systems. The weed pressure mapping is viable as basis for the UGV showcase of precision weed management. For this, we show the automated image acquisition by the UGV and a subsequent plant classification with a four-step pipeline, differentiating crop from weed in real time. Advantages and disadvantages as well as future prospects of such approaches will be discussed.

@InProceedings{walter2018icpa,
Title = {Flourish - A robotic approach for automation in crop management},
Author = {A. Walter and R. Khanna and P. Lottes and C. Stachniss and R. Siegwart and J. Nieto and F. Liebisch},
Booktitle = icpa,
Year = 2018,
abstract = {The Flourish project aims to bridge the gap between current and desired capabilities of agricultural robots by developing an adaptable robotic solution for precision farming. Combining the aerial survey capabilities of a small autonomous multi-copter Unmanned Aerial Vehicle (UAV) with a multi-purpose agricultural Unmanned Ground Vehicle (UGV), the system will be able to survey a field from the air, perform targeted intervention on the ground, and provide detailed information for decision support, all with minimal user intervention. The system can be adapted to a wide range of farm management activities and to different crops by choosing different sensors, status indicators and ground treatment packages. The research project thereby touches a selection of topics addressed by ICPA such as sensor application in managing in-season crop variability, precision nutrient management and crop protection as well as remote sensing applications in precision agriculture and engineering technologies and advances. This contribution will introduce the Flourish consortium and concept using the results of three years of active development, testing, and measuring in field campaigns. Two key parts of the project will be shown in more detail: First, mapping of the field by drones for detection of sugar beet nitrogen status variation and weed pressure in the field and second the perception of the UGV as related to weed classification and subsequent precision weed management. The field mapping by means of an UAV will be shown for crop nitrogen status estimation and weed pressure with examples for subsequent crop management decision support. For nitrogen status, the results indicate that drones are up to the task to deliver crop nitrogen variability maps utilized for variable rate application that are of comparable quality to current on-tractor systems. The weed pressure mapping is viable as basis for the UGV showcase of precision weed management. For this, we show the automated image acquisition by the UGV and a subsequent plant classification with a four-step pipeline, differentiating crop from weed in real time. Advantages and disadvantages as well as future prospects of such approaches will be discussed.},
}

I. Bogoslavskyi and C. Stachniss, “Analyzing the Quality of Matched 3D Point Clouds of Objects,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2017. [BibTeX][PDF]

The ability to extract individual objects in the scene is key for a large number of autonomous navigation systems such as mobile robots or autonomous cars. Such systems navigating in dynamic environments need to be aware of objects that may change or move. In most perception cues, a pre-segmentation of the current image or laser scan into individual objects is the first processing step before a further analysis is performed. In this paper, we present an effective method that first removes the ground from the scan and then segments the 3D data in a range image representation into different objects. A key focus of our work is a fast execution with several hundred Hertz. Our implementation has small computational demands so that it can run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud and operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan. This approach can furthermore handle sparse 3D data well, which is important for scanners such as the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS, thoroughly tested it using different 3D scanners, and will release the source code of our implementation. Our method can operate at frame rates that are substantially higher than those of the sensors while using only a single core of a mobile CPU and producing high-quality segmentation results.

@InProceedings{bogoslavskyi2017pfg,
title = {Efficient Online Segmentation for Sparse 3D Laser Scans},
author = {Bogoslavskyi, Igor and Stachniss, Cyrill},
booktitle = pfg,
year = {2017},
pages = {41--52},
volume = {85},
issue = {1},
abstract = {The ability to extract individual objects in the scene is key for a large number of autonomous navigation systems such as mobile robots or autonomous cars. Such systems navigating in dynamic environments need to be aware of objects that may change or move. In most perception cues, a pre-segmentation of the current image or laser scan into individual objects is the first processing step before a further analysis is performed. In this paper, we present an effective method that first removes the ground from the scan and then segments the 3D data in a range image representation into different objects. A key focus of our work is a fast execution with several hundred Hertz. Our implementation has small computational demands so that it can run online on most mobile systems. We explicitly avoid the computation of the 3D point cloud and operate directly on a 2.5D range image, which enables a fast segmentation for each 3D scan. This approach can furthermore handle sparse 3D data well, which is important for scanners such as the new Velodyne VLP-16 scanner. We implemented our approach in C++ and ROS, thoroughly tested it using different 3D scanners, and will release the source code of our implementation. Our method can operate at frame rates that are substantially higher than those of the sensors while using only a single core of a mobile CPU and producing high-quality segmentation results.},
url = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi16pfg.pdf},
codeurl = {https://github.com/Photogrammetry-Robotics-Bonn/depth_clustering},
videourl = {https://www.youtube.com/watch?v=6WqsOlHGTLA},
}

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

@InProceedings{milioto2017uavg,
title = {Real-time Blob-wise Sugar Beets vs Weeds Classification for Monitoring Fields using Convolutional Neural Networks},
author = {A. Milioto and P. Lottes and C. Stachniss},
booktitle = uavg,
year = {2017},
abstract = {UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.},
url = {http://www.ipb.uni-bonn.de/pdfs/milioto17uavg.pdf},
}

Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences

@InProceedings{nardi2017jras,
title = {User Preferred Behaviors for Robot Navigation Exploiting Previous Experiences},
author = {L. Nardi and C. Stachniss},
booktitle = jras,
year = {2017},
doi = {10.1016/j.robot.2017.08.014},
abstract = {Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences},
url = {http://www.ipb.uni-bonn.de/pdfs/nardi17jras.pdf},
}

Bundle adjustment is a central part of most visual SLAM and Structure from Motion systems and thus a relevant component of UAVs equipped with cameras. This paper makes two contributions to bundle adjustment. First, we present a novel approach which exploits trifocal constraints, i.e., constraints resulting from corresponding points observed in three camera images, which allows to estimate the camera pose parameters without 3D point estimation. Second, we analyze the quality loss compared to the optimal bundle adjustment solution when applying different types of approximations to the constrained optimization problem to increase efficiency. We implemented and thoroughly evaluated our approach using a UAV performing mapping tasks in outdoor environments. Our results indicate that the complexity of the constraint bundle adjustment can be decreased without loosing too much accuracy.

@InProceedings{schneider2017uavg,
title = {On the Quality and Efficiency of Approximate Solutions to Bundle Adjustment with Epipolar and Trifocal Constraints},
author = {J. Schneider and C. Stachniss and W. F\"orstner},
booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2017},
pages = {81-88},
volume = {IV-2/W3},
abstract = {Bundle adjustment is a central part of most visual SLAM and Structure from Motion systems and thus a relevant component of UAVs equipped with cameras. This paper makes two contributions to bundle adjustment. First, we present a novel approach which exploits trifocal constraints, i.e., constraints resulting from corresponding points observed in three camera images, which allows to estimate the camera pose parameters without 3D point estimation. Second, we analyze the quality loss compared to the optimal bundle adjustment solution when applying different types of approximations to the constrained optimization problem to increase efficiency. We implemented and thoroughly evaluated our approach using a UAV performing mapping tasks in outdoor environments. Our results indicate that the complexity of the constraint bundle adjustment can be decreased without loosing too much accuracy.},
doi = {10.5194/isprs-annals-IV-2-W3-81-2017},
url = {https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W3/81/2017/isprs-annals-IV-2-W3-81-2017.pdf},
}

Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short computation times and short paths while maximizing robot safety under certain assumptions.

@InProceedings{schirmer2017iros,
author = {R. Schirmer and P. Biber and C. Stachniss},
title = {Efficient Path Planning in Belief Space for Safe Navigation},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2017},
abstract = {Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantees in the presence of sensor/actuator noise and featureless areas in the environment. In this paper, we tackle the problem of planning a path that maximizes robot safety while navigating inside the working area and under the constraints of limited computing resources and cheap sensors. Our approach uses a map of the environment to estimate localizability at all locations, and it uses these estimates to search for a path from start to goal in belief space using an extended heuristic search algorithm. We implemented our approach using C++ and ROS and thoroughly tested it on simulation data recorded on eight different gardens, as well as on a real robot. The experiments presented in this paper show that our approach leads to short computation times and short paths while maximizing robot safety under certain assumptions.},
url = {http://www.ipb.uni-bonn.de/pdfs/schirmer17iros.pdf},
}

We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.

@Article{beekmans16acp,
title = {Cloud Photogrammetry with Dense Stereo for Fisheye Cameras},
author = {C. Beekmans and J. Schneider and T. L\"abe and M. Lennefer and C. Stachniss and C. Simmer},
journal = {Atmospheric Chemistry and Physics (ACP)},
year = {2016},
number = {22},
pages = {14231-14248},
volume = {16},
abstract = {We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.},
doi = {10.5194/acp-16-14231-2016},
url = {http://www.ipb.uni-bonn.de/pdfs/beekmans16acp.pdf},
}

The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.

@InProceedings{nardi16iros,
title = {Experience-Based Path Planning for Mobile Robots Exploiting User Preferences},
author = {L. Nardi and C. Stachniss},
booktitle = iros,
year = {2016},
doi = {10.1109/IROS.2016.7759197},
abstract = {The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often requested. In this paper, we propose an easy-to-use motion planning scheme that can take into account user preferences for robot navigation. The preferences are extracted implicitly from the previous experiences or from demonstrations and are automatically considered in the subsequent planning steps. This leads to reproducible and thus better to predict navigation behaviors of the robot, without requiring experts to hard-coding control strategies or cost functions within a planner. Our system has been implemented and evaluated on a simulated KUKA mobile robot in different environments.},
url = {http://www.ipb.uni-bonn.de/pdfs/nardi16iros.pdf},
}

Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.

@InProceedings{schneider16icra,
title = {Fast and Effective Online Pose Estimation and Mapping for UAVs},
author = {J. Schneider and C. Eling and L. Klingbeil and H. Kuhlmann and W. F\"orstner and C. Stachniss},
booktitle = icra,
year = {2016},
pages = {4784--4791},
abstract = {Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.},
doi = {10.1109/ICRA.2016.7487682},
url = {http://www.ipb.uni-bonn.de/pdfs/schneider16icra.pdf},
}

Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and this renders the computation of depth information from fisheye stereo image pairs more complicated. In this work, we analyze the combination of an epipolar rectification model for fisheye stereo cameras with existing dense methods. This has the advantage that existing dense stereo systems can be applied as a black-box even with cameras that have field of view of more than 180 deg to obtain dense disparity information. We thoroughly investigate the accuracy potential of such fisheye stereo systems using image data from our UAV. The empirical analysis is based on image pairs of a calibrated fisheye stereo camera system and two state-of-the-art algorithms for dense stereo applied to adequately rectified image pairs from fisheye stereo cameras. The canonical stochastic model for sensor points assumes homogeneous uncertainty and we generalize this model based on an empirical analysis using a test scene consisting of mutually orthogonal planes. We show (1) that the combination of adequately rectified fisheye image pairs and dense methods provides dense 3D point clouds at 6-7 Hz on our autonomous multi-copter UAV, (2) that the uncertainty of points depends on their angular distance from the optical axis, (3) how to estimate the variance component as a function of that distance, and (4) how the improved stochastic model improves the accuracy of the scene points.

@Article{schneider16ral,
title = {On the Accuracy of Dense Fisheye Stereo},
author = {J. Schneider and C. Stachniss and W. F\"orstner},
journal = {IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics \& Automation (ICRA)},
year = {2016},
number = {1},
pages = {227-234},
volume = {1},
abstract = {Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and this renders the computation of depth information from fisheye stereo image pairs more complicated. In this work, we analyze the combination of an epipolar rectification model for fisheye stereo cameras with existing dense methods. This has the advantage that existing dense stereo systems can be applied as a black-box even with cameras that have field of view of more than 180 deg to obtain dense disparity information. We thoroughly investigate the accuracy potential of such fisheye stereo systems using image data from our UAV. The empirical analysis is based on image pairs of a calibrated fisheye stereo camera system and two state-of-the-art algorithms for dense stereo applied to adequately rectified image pairs from fisheye stereo cameras. The canonical stochastic model for sensor points assumes homogeneous uncertainty and we generalize this model based on an empirical analysis using a test scene consisting of mutually orthogonal planes. We show (1) that the combination of adequately rectified fisheye image pairs and dense methods provides dense 3D point clouds at 6-7 Hz on our autonomous multi-copter UAV, (2) that the uncertainty of points depends on their angular distance from the optical axis, (3) how to estimate the variance component as a function of that distance, and (4) how the improved stochastic model improves the accuracy of the scene points.},
doi = {10.1109/LRA.2016.2516509},
url = {http://www.ipb.uni-bonn.de/pdfs/schneider16ral.pdf},
}

The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.

@InProceedings{schubert2016investigation,
title = {{Investigation of Latent Traces Using Infrared Reflectance Hyperspectral Imaging}},
author = {Schubert, Till and Wenzel, Susanne and Roscher, Ribana and Stachniss, Cyrill},
booktitle = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
year = {2016},
pages = {97--102},
volume = {III-7},
abstract = {The detection of traces is a main task of forensic science. A potential method is hyperspectral imaging (HSI) from which we expect to capture more fluorescence effects than with common Forensic Light Sources (FLS). Specimen of blood, semen and saliva traces in several dilution steps are prepared on cardboard substrate. As our key result we successfully make latent traces visible up to highest available dilution (1:8000). We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the Shortwave Infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood (ML) classifier assuming normally distributed data. Random Forest builds a competitive approach. The classifiers retrieve the exact positions of labeled trace preparation up to highest dilution and determine posterior probabilities. By modeling the classification with a Markov Random Field we obtain smoothed results.},
doi = {10.5194/isprs-annals-III-7-97-2016},
url = {http://www.ipb.uni-bonn.de/pdfs/Schubert2016Investigation.pdf},
}

O. Vysotska and C. Stachniss, “Exploiting Building Information from Publicly Available Maps in Graph-Based SLAM,” in Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) , 2016. [BibTeX][PDF]

Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons

@Article{vysotska16ral,
title = {Lazy Data Association For Image Sequences Matching Under Substantial Appearance Changes},
author = {O. Vysotska and C. Stachniss},
journal = {IEEE Robotics and Automation Letters (RA-L)and IEEE International Conference on Robotics \& Automation (ICRA)},
year = {2016},
number = {1},
pages = {1-8},
volume = {1},
abstract = {Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under different weather conditions. In this letter, we investigate the problem of efficiently coping with seasonal appearance changes in online localization. We propose a lazy data association approach for matching streams of incoming images to a reference image sequence in an online fashion. We present a search heuristic to quickly find matches between the current image sequence and a database using a data association graph. Our experiments conducted under substantial seasonal changes suggest that our approach can efficiently match image sequences while requiring a comparably small number of image to image comparisons},
doi = {10.1109/LRA.2015.2512936},
timestamp = {2016.04.18},
url = {http://www.ipb.uni-bonn.de/pdfs/vysotska16ral-icra.pdf},
codeurl = {https://github.com/Photogrammetry-Robotics-Bonn/online_place_recognition},
videourl = {https://www.youtube.com/watch?v=l-hNk7Z4lSk},
}

As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, cultural background, or common sense. Obviously, such factors are hard to formulate or model a priori. In this paper, we present a solution for tidying up objects in containers, e.g., shelves or boxes, by following user preferences. We learn the user preferences using collaborative filtering based on crowdsourced and mined data. First, we predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsoucing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.

@InProceedings{abdo15icra,
title = {Robot, Organize my Shelves! Tidying up Objects by Predicting User Preferences},
author = {N. Abdo and C. Stachniss and L. Spinello and W.Burgard},
booktitle = icra,
year = {2015},
pages = {1557-1564},
abstract = {As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, cultural background, or common sense. Obviously, such factors are hard to formulate or model a priori. In this paper, we present a solution for tidying up objects in containers, e.g., shelves or boxes, by following user preferences. We learn the user preferences using collaborative filtering based on crowdsourced and mined data. First, we predict pairwise object preferences of the user. Then, we subdivide the objects in containers by modeling a spectral clustering problem. Our solution is easy to update, does not require complex modeling, and improves with the amount of user data. We evaluate our approach using crowdsoucing data from over 1,200 users and demonstrate its effectiveness for two tidy-up scenarios. Additionally, we show that a real robot can reliably predict user preferences using our approach.},
doi = {10.1109/ICRA.2015.7139396},
url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/abdo15icra.pdf},
}

Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route planning. We propose an MDP-based planner that considers route information as well as the occupancy probabilities of parking spaces to compute the path that minimizes the expected total time for finding an unoccupied parking space and for walking from the parking location to the target destination. We evaluated our system on real world data gathered over several days in a real parking lot. We furthermore compare our approach to three parking strategies and show that our method outperforms the alternative behaviors.

@InProceedings{bogoslavskyi15icra,
title = {Where to Park? Minimizing the Expected Time to Find a Parking Space},
author = {I. Bogoslavskyi and L. Spinello and W. Burgard and C. Stachniss},
booktitle = icra,
year = {2015},
pages = {2147-2152},
abstract = {Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route planning. We propose an MDP-based planner that considers route information as well as the occupancy probabilities of parking spaces to compute the path that minimizes the expected total time for finding an unoccupied parking space and for walking from the parking location to the target destination. We evaluated our system on real world data gathered over several days in a real parking lot. We furthermore compare our approach to three parking strategies and show that our method outperforms the alternative behaviors.},
doi = {10.1109/ICRA.2015.7139482},
timestamp = {2015.06.29},
url = {http://www.ipb.uni-bonn.de/pdfs/bogoslavskyi15icra.pdf},
}

In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.

@InProceedings{naseer15iros,
title = {Robust Visual SLAM Across Seasons},
author = {Naseer, Tayyab and Ruhnke, Michael and Spinello, Luciano and Stachniss, Cyrill and Burgard, Wolfram},
booktitle = iros,
year = {2015},
pages = {2529 - 2535},
abstract = {In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix by comparing each image of a query sequence with a database. Finally, based on the similarity matrix, we formulate a flow network problem and compute matching hypotheses between sequences. In this way, our approach can handle partially matching routes, loops in the trajectory and different speeds of the robot. With a matching hypothesis as loop closure information and the odometry information of the robot, we formulate a graph based SLAM problem and compute a joint maximum likelihood trajectory.},
doi = {10.1109/IROS.2015.7353721},
timestamp = {2016.04.19},
url = {http://www.ipb.uni-bonn.de/pdfs/Naseer2015Robust.pdf},
}

The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot’s belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.

@InProceedings{perea15icra,
title = {Predictive Exploration Considering Previously Mapped Environments},
author = {D. Perea-Str{\"o}m and F. Nenci and C. Stachniss},
booktitle = icra,
year = {2015},
pages = {2761-2766},
abstract = {The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the problem of how to select view points that support the underlying mapping process. We propose a novel approach that makes predictions about the structure of the environments in the unexplored areas by relying on maps acquired previously. Our approach seeks to find similarities between the current surroundings of the robot and previously acquired maps stored in a database in order to predict how the environment may expand in the unknown areas. This allows us to predict potential future loop closures early. This knowledge is used in the view point selection to actively close loops and in this way reduce the uncertainty in the robot's belief. We implemented and tested the proposed approach. The experiments indicate that our method improves the ability of a robot to explore challenging environments and improves the quality of the resulting maps.},
doi = {10.1109/ICRA.2015.7139574},
url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/perea15icra.pdf},
}

The ability to simultaneously localize a robot and build a map of the environment is central to most robotics applications, and the problem is often referred to as simultaneous localization and mapping (SLAM). Robotics researchers have proposed a large variety of solutions allowing robots to build maps and use them for navigation. In addition, the geodetic community has addressed large-scale map building for centuries, computing maps that span across continents. These large-scale mapping processes had to deal with several challenges that are similar to those of the robotics community. In this article, we explain key geodetic map building methods that we believe are relevant for robot mapping. We also aim at providing a geodetic perspective on current state-of-the-art SLAM methods and identifying similarities both in terms of challenges faced and the solutions proposed by both communities. The central goal of this article is to connect both fields and enable future synergies between them.

@Article{agarwal2014b,
title = {A Survey of Geodetic Approaches to Mapping and the Relationship to Graph-Based SLAM},
author = {Pratik Agarwal and Wolfram Burgard and Cyrill Stachniss},
journal = {IEEE Robotics and Automation Magazine},
year = {2014},
pages = {63 - 80},
volume = {21},
abstract = {The ability to simultaneously localize a robot and build a map of the environment is central to most robotics applications, and the problem is often referred to as simultaneous localization and mapping (SLAM). Robotics researchers have proposed a large variety of solutions allowing robots to build maps and use them for navigation. In addition, the geodetic community has addressed large-scale map building for centuries, computing maps that span across continents. These large-scale mapping processes had to deal with several challenges that are similar to those of the robotics community. In this article, we explain key geodetic map building methods that we believe are relevant for robot mapping. We also aim at providing a geodetic perspective on current state-of-the-art SLAM methods and identifying similarities both in terms of challenges faced and the solutions proposed by both communities. The central goal of this article is to connect both fields and enable future synergies between them.},
doi = {10.1109/MRA.2014.2322282},
timestamp = {2014.09.18},
url = {http://www2.informatik.uni-freiburg.de/~stachnis/pdf/agarwal14ram-preprint.pdf},
}